Wang et al. [1]} successfully reduced a wide spectrum of DNN data and computations
for training to 8-bit floating-point numbers with a potential of 2-4x improved throughput.
Recent research [2]}
in Deep Neural Networks (DNNs) demonstrates that DNNs can have more than 99% of their training
reduced to half precision with similar accuracy to corresponding unreduced training.
The comparison of unreduced, dynamically reduced, and mostly reduced trained performances
is similar to our approach, but deals with a very different machine learning model, and very
low-precision formats are not explored.
